| Country | Boundary Name | Pop-Min | Pop-Max | Household-Min | Household-Max | Total No.Scot 11’ | Total No. Glasgow 11’ |
|---|---|---|---|---|---|---|---|
| Scotland | Output Area | 50 | - | 20 | - | 46351 | 5486 |
| Datazone | 500 | 1000 | - | - | 6976 | 746 | |
| Intermediate Zone | 2500 | 6000 | - | - | 1279 | 136 | |
| England | Output Area | 100 | - | 40 | - | 181408 | - |
| Lower Super Output Area | 1000 | 3000 | 400 | 1200 | 34753 | - | |
| Middle Super Output Area | 5000 | 15000 | 2000 | 6000 | 7201 | - |
Call files: primary schools and intermediate zones
schools <-
read_sf("Glasgow/Bld_Schools_Glasgow.shp") %>%
select(-c(FID_1, FID_2)) %>%
mutate(FID = row_number()) %>%
select(FID, everything())
iz <- read_sf("Glasgow/Glasgow_IZ.shp")
code <- read_csv("Glasgow/dz2011_codes_and_labels_21042020.csv")## Rows: 6976 Columns: 11## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): DataZone, DataZoneName, IntZone, IntZoneName, CA, CAName, HSCP, HS...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Taking a glance on the data
schools %>%
st_drop_geometry() %>%
glimpse()## Rows: 318
## Columns: 81
## $ FID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
## $ id <chr> "0D4F710245D627B3E050A00A568A259B", "0D4F7107E62B27B3E050A0…
## $ featcode <dbl> 15014, 15014, 15014, 15014, 15014, 15014, 15014, 15014, 150…
## $ area <dbl> 2629, 30, 31, 39, 1613, 65, 3075, 40, 25, 29, 1664, 1413, 5…
## $ DataZone <chr> "S01010452", "S01010452", "S01010452", "S01010463", "S01010…
## $ LAName <chr> "Glasgow City", "Glasgow City", "Glasgow City", "Glasgow Ci…
## $ SAPE2014 <dbl> 785, 785, 785, 823, 823, 823, 670, 596, 596, 596, 596, 1008…
## $ WASAPE2014 <dbl> 436, 436, 436, 514, 514, 514, 401, 359, 359, 359, 359, 603,…
## $ Rank <dbl> 1554, 1554, 1554, 3317, 3317, 3317, 1285, 1692, 1692, 1692,…
## $ Quintile <dbl> 2, 2, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2,…
## $ Decile <dbl> 3, 3, 3, 5, 5, 5, 2, 3, 3, 3, 3, 5, 5, 1, 2, 1, 1, 1, 1, 3,…
## $ Vigintile <dbl> 5, 5, 5, 10, 10, 10, 4, 5, 5, 5, 5, 10, 10, 1, 3, 1, 1, 1, …
## $ Percentile <dbl> 23, 23, 23, 48, 48, 48, 19, 25, 25, 25, 25, 48, 48, 2, 14, …
## $ IncRate <dbl> 0.23, 0.23, 0.23, 0.11, 0.11, 0.11, 0.26, 0.20, 0.20, 0.20,…
## $ IncNumDep <dbl> 180, 180, 180, 85, 85, 85, 175, 120, 120, 120, 120, 110, 11…
## $ IncRank <dbl> 1092, 1092, 1092, 3325, 3325, 3325, 754, 1403, 1403, 1403, …
## $ EmpRate <dbl> 0.19, 0.19, 0.19, 0.10, 0.10, 0.10, 0.16, 0.16, 0.16, 0.16,…
## $ EmpNumDep <dbl> 85, 85, 85, 50, 50, 50, 65, 60, 60, 60, 60, 55, 55, 95, 70,…
## $ EmpRank <dbl> 1113, 1113, 1113, 3059, 3059, 3059, 1689, 1587, 1587, 1587,…
## $ HlthCIF <dbl> 165, 165, 165, 130, 130, 130, 165, 165, 165, 165, 165, 115,…
## $ HlthAlcSR <dbl> 138.82534, 138.82534, 138.82534, 63.99475, 63.99475, 63.994…
## $ HlthDrugSR <dbl> 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.000…
## $ HlthSMR <dbl> 117, 117, 117, 96, 96, 96, 148, 80, 80, 80, 80, 97, 97, 208…
## $ HlthDprsPc <dbl> 0.2152866, 0.2152866, 0.2152866, 0.1822600, 0.1822600, 0.18…
## $ HlthLBWTPc <dbl> 0.02941176, 0.02941176, 0.02941176, 0.00000000, 0.00000000,…
## $ HlthEmrgSR <dbl> 109.4836, 109.4836, 109.4836, 112.8559, 112.8559, 112.8559,…
## $ HlthRank <dbl> 1734, 1734, 1734, 2731, 2731, 2731, 1549, 1855, 1855, 1855,…
## $ EduAttend <dbl> 0.8539326, 0.8539326, 0.8539326, 0.8423913, 0.8423913, 0.84…
## $ EduAttain <dbl> 5.500000, 5.500000, 5.500000, 5.083333, 5.083333, 5.083333,…
## $ EduNoQuals <dbl> 153.0186, 153.0186, 153.0186, 101.8701, 101.8701, 101.8701,…
## $ EduNEET <dbl> 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.28, 0.04, 0.04, 0.04,…
## $ EduHESA <dbl> 0.04615385, 0.04615385, 0.04615385, 0.12598425, 0.12598425,…
## $ EduRank <dbl> 2793, 2793, 2793, 3469, 3469, 3469, 1057, 2683, 2683, 2683,…
## $ GAccPetrol <dbl> 4.322612, 4.322612, 4.322612, 3.241190, 3.241190, 3.241190,…
## $ GAccDTGP <dbl> 1.2484531, 1.2484531, 1.2484531, 2.5301074, 2.5301074, 2.53…
## $ GAccDTPost <dbl> 1.892881, 1.892881, 1.892881, 1.843244, 1.843244, 1.843244,…
## $ GAccDTPsch <dbl> 1.5095087, 1.5095087, 1.5095087, 1.2702325, 1.2702325, 1.27…
## $ GAccDTRet <dbl> 3.631134, 3.631134, 3.631134, 3.581010, 3.581010, 3.581010,…
## $ GAccDTSsch <dbl> 4.962037, 4.962037, 4.962037, 3.174072, 3.174072, 3.174072,…
## $ GAccPTGP <dbl> 4.573739, 4.573739, 4.573739, 7.476432, 7.476432, 7.476432,…
## $ GAccPTPost <dbl> 5.335732, 5.335732, 5.335732, 5.875810, 5.875810, 5.875810,…
## $ GAccPTRet <dbl> 11.372774, 11.372774, 11.372774, 11.879900, 11.879900, 11.8…
## $ GAccRank <dbl> 5571, 5571, 5571, 4955, 4955, 4955, 5520, 4951, 4951, 4951,…
## $ CrimeCount <dbl> 12.01810, 12.01810, 12.01810, 20.03017, 20.03017, 20.03017,…
## $ CrimeRate <dbl> 153.0968, 153.0968, 153.0968, 243.3799, 243.3799, 243.3799,…
## $ CrimeRank <dbl> 4305, 4305, 4305, 3033, 3033, 3033, 2179, 552, 552, 552, 55…
## $ HouseNumOC <dbl> 148, 148, 148, 160, 160, 160, 110, 61, 61, 61, 61, 153, 153…
## $ HouseNumNC <dbl> 8, 8, 8, 9, 9, 9, 17, 1, 1, 1, 1, 11, 11, 10, 16, 30, 3, 3,…
## $ HouseOCrat <dbl> 0.18204182, 0.18204182, 0.18204182, 0.20100503, 0.20100503,…
## $ HouseNCrat <dbl> 0.009840098, 0.009840098, 0.009840098, 0.011306533, 0.01130…
## $ HouseRank <dbl> 1328, 1328, 1328, 1019, 1019, 1019, 1347, 3687, 3687, 3687,…
## $ Shape_Leng <dbl> 2953.927, 2953.927, 2953.927, 2850.157, 2850.157, 2850.157,…
## $ Shape_Area <dbl> 142069.8, 142069.8, 142069.8, 180664.8, 180664.8, 180664.8,…
## $ AB <dbl> 54, 54, 54, 118, 118, 118, 62, 41, 41, 41, 41, 58, 58, 6, 1…
## $ C1 <dbl> 180, 180, 180, 136, 136, 136, 96, 120, 120, 120, 120, 199, …
## $ C2 <dbl> 109, 109, 109, 119, 119, 119, 70, 93, 93, 93, 93, 175, 175,…
## $ DE <dbl> 131, 131, 131, 116, 116, 116, 183, 105, 105, 105, 105, 171,…
## $ OID_ <dbl> 3946, 3946, 3946, 3957, 3957, 3957, 3965, 3974, 3974, 3974,…
## $ All_age <dbl> 813, 813, 813, 796, 796, 796, 666, 623, 623, 623, 623, 1010…
## $ X0_4 <dbl> 36, 36, 36, 44, 44, 44, 40, 25, 25, 25, 25, 62, 62, 47, 54,…
## $ X5_7 <dbl> 25, 25, 25, 14, 14, 14, 28, 17, 17, 17, 17, 33, 33, 24, 37,…
## $ X8_9 <dbl> 14, 14, 14, 19, 19, 19, 12, 8, 8, 8, 8, 28, 28, 18, 11, 19,…
## $ X10_14 <dbl> 30, 30, 30, 44, 44, 44, 36, 55, 55, 55, 55, 58, 58, 48, 47,…
## $ F7 <dbl> 7, 7, 7, 10, 10, 10, 14, 13, 13, 13, 13, 14, 14, 13, 12, 16…
## $ X16_17 <dbl> 22, 22, 22, 24, 24, 24, 11, 17, 17, 17, 17, 16, 16, 25, 20,…
## $ X18_19 <dbl> 15, 15, 15, 20, 20, 20, 16, 15, 15, 15, 15, 25, 25, 26, 23,…
## $ X20_24 <dbl> 51, 51, 51, 39, 39, 39, 31, 19, 19, 19, 19, 52, 52, 34, 48,…
## $ X25_29 <dbl> 31, 31, 31, 36, 36, 36, 51, 27, 27, 27, 27, 39, 39, 27, 60,…
## $ X30_44 <dbl> 148, 148, 148, 136, 136, 136, 137, 126, 126, 126, 126, 173,…
## $ X45_59 <dbl> 153, 153, 153, 191, 191, 191, 127, 119, 119, 119, 119, 233,…
## $ X60_64 <dbl> 54, 54, 54, 43, 43, 43, 38, 38, 38, 38, 38, 65, 65, 16, 34,…
## $ X65_74 <dbl> 95, 95, 95, 78, 78, 78, 61, 56, 56, 56, 56, 80, 80, 54, 27,…
## $ X75_84 <dbl> 98, 98, 98, 61, 61, 61, 44, 65, 65, 65, 65, 87, 87, 19, 0, …
## $ X85_89 <dbl> 25, 25, 25, 24, 24, 24, 14, 20, 20, 20, 20, 32, 32, 9, 1, 1…
## $ X90plus <dbl> 9, 9, 9, 13, 13, 13, 6, 3, 3, 3, 3, 13, 13, 2, 2, 3, 1, 1, …
## $ House_nm <dbl> 408, 408, 408, 376, 376, 376, 316, 285, 285, 285, 285, 432,…
## $ no_car <dbl> 199, 199, 199, 129, 129, 129, 149, 107, 107, 107, 107, 163,…
## $ one_car <dbl> 162, 162, 162, 178, 178, 178, 141, 127, 127, 127, 127, 173,…
## $ two_cars <dbl> 41, 41, 41, 57, 57, 57, 23, 45, 45, 45, 45, 79, 79, 12, 35,…
## $ three_car <dbl> 6, 6, 6, 12, 12, 12, 3, 6, 6, 6, 6, 17, 17, 2, 6, 1, 6, 6, …
## $ Zone <chr> "S01010452", "S01010452", "S01010452", "S01010463", "S01010…Summary stats
schools %>%
st_drop_geometry() %>%
get_summary_stats() %>%
print(n = Inf) ## # A tibble: 77 × 13
## variable n min max median q1 q3 iqr mad mean
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AB 318 0 3.2 e+2 5.15e+1 1.92e+1 9.4 e+1 7.48e+1 5.12e+1 7.23e+1
## 2 All_age 318 0 1.13e+3 7.56e+2 6.40e+2 8.86e+2 2.46e+2 1.88e+2 7.49e+2
## 3 area 318 23 8.31e+3 6.84e+2 8.12e+1 1.95e+3 1.87e+3 9.55e+2 1.08e+3
## 4 C1 318 0 5.91e+2 1.51e+2 1.05e+2 2.01e+2 9.6 e+1 6.97e+1 1.57e+2
## 5 C2 318 0 1.97e+2 9.6 e+1 6.8 e+1 1.2 e+2 5.2 e+1 3.63e+1 9.33e+1
## 6 CrimeCou… 318 0 2.19e+2 2.50e+1 1.60e+1 4.51e+1 2.90e+1 1.93e+1 3.39e+1
## 7 CrimeRank 318 0 6.89e+3 1.76e+3 7.32e+2 3.35e+3 2.62e+3 1.76e+3 2.10e+3
## 8 CrimeRate 318 0 3.10e+3 3.44e+2 1.98e+2 5.53e+2 3.56e+2 2.42e+2 4.15e+2
## 9 DE 318 0 4.02e+2 1.82e+2 1.15e+2 2.34e+2 1.19e+2 8.30e+1 1.78e+2
## 10 Decile 318 0 1 e+1 2 e+0 1 e+0 4.75e+0 3.75e+0 1.48e+0 3.00e+0
## 11 EduAttain 318 0 6.5 e+0 5.08e+0 4.57e+0 5.47e+0 8.96e-1 6.25e-1 4.72e+0
## 12 EduAttend 318 0 9.84e-1 7.68e-1 7.11e-1 8.44e-1 1.33e-1 1.06e-1 7.22e-1
## 13 EduHESA 318 0 2.34e-1 5.4 e-2 3 e-2 8.9 e-2 5.9 e-2 4.3 e-2 6.1 e-2
## 14 EduNEET 318 0 3.7 e-1 8 e-2 4 e-2 1.4 e-1 1 e-1 5.9 e-2 9.6 e-2
## 15 EduNoQua… 318 0 3.03e+2 1.58e+2 1.01e+2 2.06e+2 1.04e+2 8.02e+1 1.48e+2
## 16 EduRank 318 0 6.85e+3 1.41e+3 4.85e+2 3.14e+3 2.65e+3 1.81e+3 1.91e+3
## 17 EmpNumDep 318 0 3.2 e+2 8.5 e+1 5 e+1 1.25e+2 7.5 e+1 5.19e+1 9.11e+1
## 18 EmpRank 318 0 6.98e+3 1.13e+3 3.28e+2 2.78e+3 2.46e+3 1.39e+3 1.76e+3
## 19 EmpRate 318 0 4.1 e-1 1.7 e-1 9 e-2 2.5 e-1 1.6 e-1 1.19e-1 1.71e-1
## 20 F7 318 0 2.6 e+1 8 e+0 4 e+0 1.1 e+1 7 e+0 4.45e+0 7.91e+0
## 21 featcode 318 15014 1.50e+4 1.50e+4 1.50e+4 1.50e+4 0 0 1.50e+4
## 22 FID 318 1 3.18e+2 1.60e+2 8.02e+1 2.39e+2 1.58e+2 1.18e+2 1.60e+2
## 23 GAccDTGP 318 0 6.20e+0 1.98e+0 1.50e+0 2.64e+0 1.14e+0 8.52e-1 2.07e+0
## 24 GAccDTPo… 318 0 4.74e+0 2.13e+0 1.65e+0 2.47e+0 8.18e-1 6.71e-1 2.02e+0
## 25 GAccDTPs… 318 0 2.93e+0 1.34e+0 1.04e+0 1.67e+0 6.3 e-1 4.72e-1 1.36e+0
## 26 GAccDTRet 318 0 7.92e+0 3.31e+0 2.24e+0 3.97e+0 1.73e+0 1.29e+0 3.15e+0
## 27 GAccDTSs… 318 0 8.25e+0 3.76e+0 2.96e+0 4.77e+0 1.80e+0 1.29e+0 3.77e+0
## 28 GAccPetr… 318 0 5.20e+0 2.52e+0 1.90e+0 3.46e+0 1.56e+0 1.19e+0 2.59e+0
## 29 GAccPTGP 318 0 1.49e+1 6.63e+0 4.77e+0 8.76e+0 3.99e+0 2.83e+0 6.70e+0
## 30 GAccPTPo… 318 0 1.32e+1 6.62e+0 5.45e+0 8.07e+0 2.62e+0 1.90e+0 6.54e+0
## 31 GAccPTRet 318 0 1.97e+1 9.06e+0 6.75e+0 1.14e+1 4.62e+0 3.41e+0 8.86e+0
## 32 GAccRank 318 0 6.97e+3 4.95e+3 3.75e+3 6.00e+3 2.26e+3 1.77e+3 4.65e+3
## 33 HlthAlcSR 318 0 1.70e+3 1.39e+2 6.84e+1 2.54e+2 1.86e+2 1.41e+2 1.84e+2
## 34 HlthCIF 318 0 3.4 e+2 1.65e+2 1.1 e+2 2.15e+2 1.05e+2 7.41e+1 1.57e+2
## 35 HlthDprs… 318 0 3.34e-1 2.12e-1 1.62e-1 2.51e-1 8.9 e-2 6.5 e-2 1.99e-1
## 36 HlthDrug… 318 0 7.52e+2 7.69e+1 2.77e+1 2.06e+2 1.79e+2 1.14e+2 1.33e+2
## 37 HlthEmrg… 318 0 3.24e+2 1.26e+2 1.03e+2 1.51e+2 4.83e+1 3.71e+1 1.24e+2
## 38 HlthLBWT… 318 0 1.74e-1 5.3 e-2 2.2 e-2 9.5 e-2 7.3 e-2 5.1 e-2 5.8 e-2
## 39 HlthRank 318 0 6.29e+3 1.04e+3 2.94e+2 2.50e+3 2.21e+3 1.33e+3 1.54e+3
## 40 HlthSMR 318 0 3.12e+2 1.25e+2 8.9 e+1 1.48e+2 5.88e+1 4.30e+1 1.22e+2
## 41 House_nm 318 0 7.03e+2 3.82e+2 2.94e+2 4.46e+2 1.52e+2 1.11e+2 3.66e+2
## 42 HouseNCr… 318 0 1.3 e-1 2.4 e-2 1 e-2 4.7 e-2 3.7 e-2 2.3 e-2 3 e-2
## 43 HouseNum… 318 0 1.22e+2 1.8 e+1 8 e+0 3.48e+1 2.68e+1 1.78e+1 2.39e+1
## 44 HouseNum… 318 0 4.18e+2 1.53e+2 1.05e+2 2.22e+2 1.17e+2 8.30e+1 1.62e+2
## 45 HouseOCr… 318 0 4.81e-1 2.07e-1 1.49e-1 2.63e-1 1.14e-1 8.5 e-2 2.08e-1
## 46 HouseRank 318 0 5.86e+3 6.48e+2 2.70e+2 1.31e+3 1.04e+3 6.69e+2 1.00e+3
## 47 IncNumDep 318 0 4.65e+2 1.6 e+2 8.5 e+1 2.35e+2 1.5 e+2 1.11e+2 1.66e+2
## 48 IncRank 318 0 6.97e+3 1.05e+3 3.38e+2 2.43e+3 2.09e+3 1.25e+3 1.63e+3
## 49 IncRate 318 0 4.9 e-1 2.1 e-1 1.1 e-1 3 e-1 1.9 e-1 1.41e-1 2.07e-1
## 50 no_car 318 0 5.39e+2 1.88e+2 1.27e+2 2.58e+2 1.30e+2 9.34e+1 1.92e+2
## 51 OID_ 318 0 3.99e+3 3.59e+3 3.39e+3 3.76e+3 3.64e+2 2.62e+2 3.39e+3
## 52 one_car 318 0 2.52e+2 1.35e+2 1.02e+2 1.69e+2 6.65e+1 4.89e+1 1.33e+2
## 53 Percenti… 318 0 9.9 e+1 1.6 e+1 5 e+0 4.08e+1 3.58e+1 1.78e+1 2.52e+1
## 54 Quintile 318 0 5 e+0 1 e+0 1 e+0 2.75e+0 1.75e+0 0 1.77e+0
## 55 Rank 318 0 6.87e+3 1.06e+3 3.03e+2 2.82e+3 2.52e+3 1.25e+3 1.72e+3
## 56 SAPE2014 318 0 1.2 e+3 7.85e+2 6.44e+2 9.05e+2 2.61e+2 2.05e+2 7.51e+2
## 57 Shape_Ar… 318 0 8.82e+5 1.63e+5 8.59e+4 2.59e+5 1.73e+5 1.19e+5 2.00e+5
## 58 Shape_Le… 318 0 7.21e+3 2.50e+3 1.89e+3 2.97e+3 1.08e+3 8.11e+2 2.58e+3
## 59 three_car 318 0 4.4 e+1 4 e+0 2 e+0 7 e+0 5 e+0 4.45e+0 6.18e+0
## 60 two_cars 318 0 1.3 e+2 2.8 e+1 1.7 e+1 4.6 e+1 2.9 e+1 1.93e+1 3.48e+1
## 61 Vigintile 318 0 2 e+1 4 e+0 1 e+0 8.75e+0 7.75e+0 4.45e+0 5.40e+0
## 62 WASAPE20… 318 0 1.03e+3 5.13e+2 4.28e+2 6.22e+2 1.94e+2 1.35e+2 5.02e+2
## 63 X0_4 318 0 1.06e+2 3.9 e+1 2.9 e+1 5.2 e+1 2.3 e+1 1.78e+1 4.08e+1
## 64 X10_14 318 0 1.11e+2 3.95e+1 2.6 e+1 4.97e+1 2.38e+1 2.00e+1 3.91e+1
## 65 X16_17 318 0 4.4 e+1 1.6 e+1 1 e+1 2.2 e+1 1.2 e+1 8.90e+0 1.57e+1
## 66 X18_19 318 0 1.44e+2 1.9 e+1 1.5 e+1 2.5 e+1 1 e+1 7.41e+0 2.03e+1
## 67 X20_24 318 0 4.42e+2 5.3 e+1 4.1 e+1 7.18e+1 3.08e+1 1.93e+1 6.11e+1
## 68 X25_29 318 0 2.16e+2 5.5 e+1 3.75e+1 8.2 e+1 4.45e+1 3.11e+1 6.39e+1
## 69 X30_44 318 0 2.95e+2 1.58e+2 1.26e+2 1.95e+2 6.9 e+1 5.04e+1 1.58e+2
## 70 X45_59 318 0 2.86e+2 1.53e+2 1.18e+2 1.86e+2 6.75e+1 5.12e+1 1.46e+2
## 71 X5_7 318 0 6.5 e+1 2 e+1 1.3 e+1 2.8 e+1 1.5 e+1 1.04e+1 2.10e+1
## 72 X60_64 318 0 9.6 e+1 3.8 e+1 2.72e+1 5.1 e+1 2.38e+1 1.78e+1 3.91e+1
## 73 X65_74 318 0 1.41e+2 6 e+1 3.7 e+1 8.6 e+1 4.9 e+1 3.41e+1 6.16e+1
## 74 X75_84 318 0 1.17e+2 4.1 e+1 2.33e+1 6.35e+1 4.03e+1 3.11e+1 4.39e+1
## 75 X8_9 318 0 5 e+1 1.2 e+1 8 e+0 1.9 e+1 1.1 e+1 7.41e+0 1.37e+1
## 76 X85_89 318 0 4.3 e+1 9 e+0 4 e+0 1.6 e+1 1.2 e+1 8.90e+0 1.07e+1
## 77 X90plus 318 0 3.2 e+1 4 e+0 1 e+0 8.5 e+0 7.5 e+0 4.45e+0 5.32e+0
## # … with 3 more variables: sd <dbl>, se <dbl>, ci <dbl>Rank by IZs
left_join(schools, code, by = "DataZone") %>%
select(FID, DataZone, IntZone, IntZoneName) %>%
rename(InterZone = IntZone) -> sch
sch %>%
group_by(InterZone, IntZoneName) %>%
summarise(no_of_schools = length(FID)) -> schools_area## `summarise()` has grouped output by 'InterZone'. You can override using the `.groups` argument.schools_area$no_of_schools %>% sum## [1] 318schools_area %>%
st_drop_geometry() %>%
arrange(desc(no_of_schools)) %>%
print(n = Inf)## # A tibble: 101 × 3
## # Groups: InterZone [101]
## InterZone IntZoneName no_of_schools
## <chr> <chr> <int>
## 1 <NA> <NA> 20
## 2 S02001886 Cathcart 8
## 3 S02001909 Greenfield 8
## 4 S02001935 Anderston 8
## 5 S02001872 Langside 7
## 6 S02001894 Parkhead West and Barrowfield 7
## 7 S02001855 Drumoyne and Shieldhall 6
## 8 S02001915 Craigend and Ruchazie 6
## 9 S02001927 Dennistoun North 6
## 10 S02001946 Summerston North 6
## 11 S02001961 Whiteinch 6
## 12 S02001846 Crookston South 5
## 13 S02001902 Garrowhill West 5
## 14 S02001922 Springburn 5
## 15 S02001925 Sighthill 5
## 16 S02001843 Darnley North 4
## 17 S02001864 Pollokshields East 4
## 18 S02001865 Govanhill West 4
## 19 S02001873 Pollokshaws 4
## 20 S02001877 Merrylee and Millbrae 4
## 21 S02001884 Kingspark South 4
## 22 S02001889 Gorbals and Hutchesontown 4
## 23 S02001899 Mount Vernon North and Sandyhills 4
## 24 S02001901 Baillieston East 4
## 25 S02001911 Tollcross 4
## 26 S02001913 Carntyne 4
## 27 S02001917 Blackhill and Barmulloch East 4
## 28 S02001926 Roystonhill, Blochairn, and Provanmill 4
## 29 S02001928 Alexandra Parade 4
## 30 S02001951 North Kelvin 4
## 31 S02001972 Blairdardie East 4
## 32 S02001853 Penilee 3
## 33 S02001857 Craigton 3
## 34 S02001859 Ibrox 3
## 35 S02001860 Ibrox East and Cessnock 3
## 36 S02001861 Kinning Park and Festival Park 3
## 37 S02001863 Pollokshields West 3
## 38 S02001866 Govanhill East and Aikenhead 3
## 39 S02001870 Shawlands West 3
## 40 S02001876 Newlands 3
## 41 S02001883 Castlemilk 3
## 42 S02001888 Toryglen and Oatlands 3
## 43 S02001890 Laurieston and Tradeston 3
## 44 S02001898 Carmyle and Mount Vernon South 3
## 45 S02001908 Barlanark 3
## 46 S02001919 Balornock 3
## 47 S02001920 Barmulloch 3
## 48 S02001921 Petershill 3
## 49 S02001938 Woodside 3
## 50 S02001941 Ruchill 3
## 51 S02001945 Summerston Central and West 3
## 52 S02001947 Maryhill East 3
## 53 S02001948 Maryhill West 3
## 54 S02001952 Kelvingrove and University 3
## 55 S02001967 Knightswood East 3
## 56 S02001969 Knightswood Park East 3
## 57 S02001970 Anniesland East 3
## 58 S02001977 Drumry West 3
## 59 S02001845 Nitshill 2
## 60 S02001847 Crookston North 2
## 61 S02001849 Pollok North and East 2
## 62 S02001850 Cardonald South and East 2
## 63 S02001854 Hillington 2
## 64 S02001858 Mosspark 2
## 65 S02001862 Kingston West and Dumbreck 2
## 66 S02001874 Carnwadric West 2
## 67 S02001881 Glenwood South 2
## 68 S02001885 Kingspark North 2
## 69 S02001896 Braidfauld 2
## 70 S02001900 Baillieston West 2
## 71 S02001903 Garrowhill East and Swinton 2
## 72 S02001905 Central Easterhouse 2
## 73 S02001930 Dennistoun 2
## 74 S02001932 City Centre East 2
## 75 S02001934 City Centre South 2
## 76 S02001943 Milton West 2
## 77 S02001955 Partick 2
## 78 S02001959 Broomhill 2
## 79 S02001973 Blairdardie West 2
## 80 S02001975 Drumchapel North 2
## 81 S02001842 Darnley East 1
## 82 S02001852 Cardonald West and Central 1
## 83 S02001868 Strathbungo 1
## 84 S02001880 Carmunnock South 1
## 85 S02001882 Glenwood North 1
## 86 S02001887 Mount Florida 1
## 87 S02001892 Bridgeton 1
## 88 S02001893 Dalmarnock 1
## 89 S02001907 North Barlanark and Easterhouse South 1
## 90 S02001914 Cranhill, Lightburn and Queenslie South 1
## 91 S02001923 Springburn East and Cowlairs 1
## 92 S02001924 Cowlairs and Port Dundas 1
## 93 S02001933 City Centre West 1
## 94 S02001942 Possil Park 1
## 95 S02001953 Hillhead 1
## 96 S02001960 Victoria Park 1
## 97 S02001962 Scotstoun North and East 1
## 98 S02001964 Yoker South 1
## 99 S02001966 Knightswood West 1
## 100 S02001971 Anniesland West 1
## 101 S02001976 Drumry East 1Plot
schools_area %>% st_drop_geometry() -> sch_df
iz %>%
left_join(sch_df, by = "InterZone") -> schools_choropleth
schools_choropleth %>%
ggplot() +
geom_sf(aes(fill = no_of_schools),
show.legend = NA) +
theme_bw() +
scale_fill_continuous(low="thistle2", high="darkred",
guide="colorbar",na.value="white") +
theme(legend.position = "bottom") -> schools_plotly
ggplotly(schools_plotly)